{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在本练习中，您将尝试构建一个神经网络，让它根据一个简单的公式来预测房屋的价格。\n",
    "\n",
    "想象一下，如果房子的定价很简单，带一间卧室的房子价格是5万+5万，那么一间卧室的房子要花10万元；两间卧室的房子就要花15万元，如此类推。\n",
    "\n",
    "如何创建一个神经网络，来学习这种关系，让它会预测一个7间卧室的房子，价格接近40万。\n",
    "\n",
    "提示：如果将房价单位用10万表示（称为scale），网络判断准确性会更好。例如对于x=1，输出1，表示10万；x=2，输出1.5表示15万。神经元网络对大数值处理不是太好，一般训练数据都要经过scalling变小才行。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 加载相应的包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 初始化模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 一个神经元，数据是一维\n",
    "model = tf.keras.Sequential([keras.layers.Dense(units=1,input_shape=[1])])\n",
    "# 设置模型的优化方式为sgd随机梯度下降\n",
    "# 损失函数(又叫目标函数)为均方误差\n",
    "# model.compile(optimizer='sgd', loss='mean_squared_error') 一样的效果\n",
    "model.compile(optimizer=tf.keras.optimizers.SGD(),loss=tf.keras.losses.mean_squared_error)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模拟并训练数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 6 samples\n",
      "Epoch 1/1000\n",
      "6/6 [==============================] - 1s 129ms/sample - loss: 0.2213\n",
      "Epoch 2/1000\n",
      "6/6 [==============================] - 0s 333us/sample - loss: 0.1255\n",
      "Epoch 3/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0810\n",
      "Epoch 4/1000\n",
      "6/6 [==============================] - 0s 498us/sample - loss: 0.0603\n",
      "Epoch 5/1000\n",
      "6/6 [==============================] - 0s 333us/sample - loss: 0.0505\n",
      "Epoch 6/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0458\n",
      "Epoch 7/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0435\n",
      "Epoch 8/1000\n",
      "6/6 [==============================] - 0s 333us/sample - loss: 0.0422\n",
      "Epoch 9/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0415\n",
      "Epoch 10/1000\n",
      "6/6 [==============================] - 0s 498us/sample - loss: 0.0410\n",
      "Epoch 11/1000\n",
      "6/6 [==============================] - 0s 333us/sample - loss: 0.0406\n",
      "Epoch 12/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0403\n",
      "Epoch 13/1000\n",
      "6/6 [==============================] - 0s 498us/sample - loss: 0.0400\n",
      "Epoch 14/1000\n",
      "6/6 [==============================] - 0s 333us/sample - loss: 0.0397\n",
      "Epoch 15/1000\n",
      "6/6 [==============================] - 0s 665us/sample - loss: 0.0394\n",
      "Epoch 16/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0391\n",
      "Epoch 17/1000\n",
      "6/6 [==============================] - 0s 328us/sample - loss: 0.0388\n",
      "Epoch 18/1000\n",
      "6/6 [==============================] - 0s 498us/sample - loss: 0.0385\n",
      "Epoch 19/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0382\n",
      "Epoch 20/1000\n",
      "6/6 [==============================] - 0s 831us/sample - loss: 0.0379\n",
      "Epoch 21/1000\n",
      "6/6 [==============================] - 0s 498us/sample - loss: 0.0377\n",
      "Epoch 22/1000\n",
      "6/6 [==============================] - 0s 494us/sample - loss: 0.0374\n",
      "Epoch 23/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0371\n",
      "Epoch 24/1000\n",
      "6/6 [==============================] - 0s 665us/sample - loss: 0.0369\n",
      "Epoch 25/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0366\n",
      "Epoch 26/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0363\n",
      "Epoch 27/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0361\n",
      "Epoch 28/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0358\n",
      "Epoch 29/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0355\n",
      "Epoch 30/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0353\n",
      "Epoch 31/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0350\n",
      "Epoch 32/1000\n",
      "6/6 [==============================] - 0s 665us/sample - loss: 0.0348\n",
      "Epoch 33/1000\n",
      "6/6 [==============================] - 0s 498us/sample - loss: 0.0345\n",
      "Epoch 34/1000\n",
      "6/6 [==============================] - 0s 498us/sample - loss: 0.0343\n",
      "Epoch 35/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0340\n",
      "Epoch 36/1000\n",
      "6/6 [==============================] - 0s 997us/sample - loss: 0.0338\n",
      "Epoch 37/1000\n",
      "6/6 [==============================] - 0s 665us/sample - loss: 0.0335\n",
      "Epoch 38/1000\n",
      "6/6 [==============================] - 0s 997us/sample - loss: 0.0333\n",
      "Epoch 39/1000\n",
      "6/6 [==============================] - 0s 665us/sample - loss: 0.0330\n",
      "Epoch 40/1000\n",
      "6/6 [==============================] - 0s 498us/sample - loss: 0.0328\n",
      "Epoch 41/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0325\n",
      "Epoch 42/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0323\n",
      "Epoch 43/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0321\n",
      "Epoch 44/1000\n",
      "6/6 [==============================] - 0s 665us/sample - loss: 0.0318\n",
      "Epoch 45/1000\n",
      "6/6 [==============================] - 0s 498us/sample - loss: 0.0316\n",
      "Epoch 46/1000\n",
      "6/6 [==============================] - 0s 498us/sample - loss: 0.0314\n",
      "Epoch 47/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0311\n",
      "Epoch 48/1000\n",
      "6/6 [==============================] - 0s 498us/sample - loss: 0.0309\n",
      "Epoch 49/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0307\n",
      "Epoch 50/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0305\n",
      "Epoch 51/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0302\n",
      "Epoch 52/1000\n",
      "6/6 [==============================] - 0s 831us/sample - loss: 0.0300\n",
      "Epoch 53/1000\n",
      "6/6 [==============================] - 0s 333us/sample - loss: 0.0298\n",
      "Epoch 54/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0296\n",
      "Epoch 55/1000\n",
      "6/6 [==============================] - 0s 2ms/sample - loss: 0.0294\n",
      "Epoch 56/1000\n",
      "6/6 [==============================] - 0s 831us/sample - loss: 0.0292\n",
      "Epoch 57/1000\n",
      "6/6 [==============================] - 0s 665us/sample - loss: 0.0290\n",
      "Epoch 58/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0287\n",
      "Epoch 59/1000\n",
      "6/6 [==============================] - 0s 665us/sample - loss: 0.0285\n",
      "Epoch 60/1000\n",
      "6/6 [==============================] - 0s 333us/sample - loss: 0.0283\n",
      "Epoch 61/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0281\n",
      "Epoch 62/1000\n",
      "6/6 [==============================] - 0s 665us/sample - loss: 0.0279\n",
      "Epoch 63/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0277\n",
      "Epoch 64/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0275\n",
      "Epoch 65/1000\n",
      "6/6 [==============================] - 0s 498us/sample - loss: 0.0273\n",
      "Epoch 66/1000\n",
      "6/6 [==============================] - 0s 831us/sample - loss: 0.0271\n",
      "Epoch 67/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0269\n",
      "Epoch 68/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0267\n",
      "Epoch 69/1000\n",
      "6/6 [==============================] - 0s 498us/sample - loss: 0.0265\n",
      "Epoch 70/1000\n",
      "6/6 [==============================] - 0s 665us/sample - loss: 0.0263\n",
      "Epoch 71/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0261\n",
      "Epoch 72/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0259\n",
      "Epoch 73/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0258\n",
      "Epoch 74/1000\n",
      "6/6 [==============================] - 0s 831us/sample - loss: 0.0256\n",
      "Epoch 75/1000\n",
      "6/6 [==============================] - 0s 665us/sample - loss: 0.0254\n",
      "Epoch 76/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0252\n",
      "Epoch 77/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0250\n",
      "Epoch 78/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0248\n",
      "Epoch 79/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0246\n",
      "Epoch 80/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0245\n",
      "Epoch 81/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0243\n",
      "Epoch 82/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0241\n",
      "Epoch 83/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0239\n",
      "Epoch 84/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0238\n",
      "Epoch 85/1000\n",
      "6/6 [==============================] - 0s 333us/sample - loss: 0.0236\n",
      "Epoch 86/1000\n",
      "6/6 [==============================] - 0s 333us/sample - loss: 0.0234\n",
      "Epoch 87/1000\n",
      "6/6 [==============================] - 0s 498us/sample - loss: 0.0232\n",
      "Epoch 88/1000\n",
      "6/6 [==============================] - 0s 333us/sample - loss: 0.0231\n",
      "Epoch 89/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0229\n",
      "Epoch 90/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0227\n",
      "Epoch 91/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0226\n",
      "Epoch 92/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0224\n",
      "Epoch 93/1000\n",
      "6/6 [==============================] - 0s 665us/sample - loss: 0.0223\n",
      "Epoch 94/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0221\n",
      "Epoch 95/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0219\n",
      "Epoch 96/1000\n",
      "6/6 [==============================] - 0s 333us/sample - loss: 0.0218\n",
      "Epoch 97/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0216\n",
      "Epoch 98/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0215\n",
      "Epoch 99/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0213\n",
      "Epoch 100/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0211\n",
      "Epoch 101/1000\n",
      "6/6 [==============================] - 0s 665us/sample - loss: 0.0210\n",
      "Epoch 102/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0208\n",
      "Epoch 103/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0207\n",
      "Epoch 104/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0205\n",
      "Epoch 105/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0204\n",
      "Epoch 106/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0202\n",
      "Epoch 107/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0201\n",
      "Epoch 108/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0199\n",
      "Epoch 109/1000\n",
      "6/6 [==============================] - 0s 333us/sample - loss: 0.0198\n",
      "Epoch 110/1000\n",
      "6/6 [==============================] - 0s 665us/sample - loss: 0.0196\n",
      "Epoch 111/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0195\n",
      "Epoch 112/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0194\n",
      "Epoch 113/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0192\n",
      "Epoch 114/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0191\n",
      "Epoch 115/1000\n",
      "6/6 [==============================] - 0s 831us/sample - loss: 0.0189\n",
      "Epoch 116/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0188\n",
      "Epoch 117/1000\n",
      "6/6 [==============================] - 0s 498us/sample - loss: 0.0187\n",
      "Epoch 118/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0185\n",
      "Epoch 119/1000\n",
      "6/6 [==============================] - 0s 665us/sample - loss: 0.0184\n",
      "Epoch 120/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0183\n",
      "Epoch 121/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0181\n",
      "Epoch 122/1000\n",
      "6/6 [==============================] - 0s 333us/sample - loss: 0.0180\n",
      "Epoch 123/1000\n",
      "6/6 [==============================] - 0s 166us/sample - loss: 0.0179\n",
      "Epoch 124/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0177\n",
      "Epoch 125/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0176\n",
      "Epoch 126/1000\n",
      "6/6 [==============================] - 0s 333us/sample - loss: 0.0175\n",
      "Epoch 127/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0174\n",
      "Epoch 128/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0172\n",
      "Epoch 129/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0171\n",
      "Epoch 130/1000\n",
      "6/6 [==============================] - 0s 665us/sample - loss: 0.0170\n",
      "Epoch 131/1000\n",
      "6/6 [==============================] - 0s 333us/sample - loss: 0.0169\n",
      "Epoch 132/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0167\n",
      "Epoch 133/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0166\n",
      "Epoch 134/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0165\n",
      "Epoch 135/1000\n",
      "6/6 [==============================] - 0s 831us/sample - loss: 0.0164\n",
      "Epoch 136/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0162\n",
      "Epoch 137/1000\n",
      "6/6 [==============================] - 0s 333us/sample - loss: 0.0161\n",
      "Epoch 138/1000\n",
      "6/6 [==============================] - 0s 333us/sample - loss: 0.0160\n",
      "Epoch 139/1000\n",
      "6/6 [==============================] - 0s 498us/sample - loss: 0.0159\n",
      "Epoch 140/1000\n",
      "6/6 [==============================] - 0s 333us/sample - loss: 0.0158\n",
      "Epoch 141/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0157\n",
      "Epoch 142/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0156\n",
      "Epoch 143/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0154\n",
      "Epoch 144/1000\n",
      "6/6 [==============================] - 0s 665us/sample - loss: 0.0153\n",
      "Epoch 145/1000\n",
      "6/6 [==============================] - 0s 498us/sample - loss: 0.0152\n",
      "Epoch 146/1000\n",
      "6/6 [==============================] - 0s 333us/sample - loss: 0.0151\n",
      "Epoch 147/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0150\n",
      "Epoch 148/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0149\n",
      "Epoch 149/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0148\n",
      "Epoch 150/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0147\n",
      "Epoch 151/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0146\n",
      "Epoch 152/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0145\n",
      "Epoch 153/1000\n",
      "6/6 [==============================] - 0s 665us/sample - loss: 0.0143\n",
      "Epoch 154/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0142\n",
      "Epoch 155/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0141\n",
      "Epoch 156/1000\n",
      "6/6 [==============================] - 0s 333us/sample - loss: 0.0140\n",
      "Epoch 157/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0139\n",
      "Epoch 158/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0138\n",
      "Epoch 159/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0137\n",
      "Epoch 160/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0136\n",
      "Epoch 161/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0135\n",
      "Epoch 162/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0134\n",
      "Epoch 163/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0133\n",
      "Epoch 164/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0132\n",
      "Epoch 165/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0131\n",
      "Epoch 166/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0130\n",
      "Epoch 167/1000\n",
      "6/6 [==============================] - 0s 498us/sample - loss: 0.0130\n",
      "Epoch 168/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0129\n",
      "Epoch 169/1000\n",
      "6/6 [==============================] - 0s 1ms/sample - loss: 0.0128\n",
      "Epoch 170/1000\n",
      "6/6 [==============================] - 0s 333us/sample - loss: 0.0127\n",
      "Epoch 171/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0126\n",
      "Epoch 172/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0125\n",
      "Epoch 173/1000\n",
      "6/6 [==============================] - 0s 665us/sample - loss: 0.0124\n",
      "Epoch 174/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0123\n",
      "Epoch 175/1000\n",
      "6/6 [==============================] - 0s 333us/sample - loss: 0.0122\n",
      "Epoch 176/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0121\n",
      "Epoch 177/1000\n",
      "6/6 [==============================] - 0s 333us/sample - loss: 0.0120\n",
      "Epoch 178/1000\n",
      "6/6 [==============================] - 0s 498us/sample - loss: 0.0120\n",
      "Epoch 179/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0119\n",
      "Epoch 180/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0118\n",
      "Epoch 181/1000\n",
      "6/6 [==============================] - 0s 333us/sample - loss: 0.0117\n",
      "Epoch 182/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 0.0116\n",
      "Epoch 183/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 0.0115\n",
      "Epoch 184/1000\n",
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      "Epoch 185/1000\n",
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      "Epoch 978/1000\n",
      "6/6 [==============================] - 0s 831us/sample - loss: 3.4426e-05\n",
      "Epoch 979/1000\n",
      "6/6 [==============================] - 0s 665us/sample - loss: 3.4174e-05\n",
      "Epoch 980/1000\n",
      "6/6 [==============================] - 0s 831us/sample - loss: 3.3925e-05\n",
      "Epoch 981/1000\n",
      "6/6 [==============================] - 0s 998us/sample - loss: 3.3679e-05\n",
      "Epoch 982/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 3.3433e-05\n",
      "Epoch 983/1000\n",
      "6/6 [==============================] - 0s 665us/sample - loss: 3.3189e-05\n",
      "Epoch 984/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 3.2948e-05\n",
      "Epoch 985/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 3.2708e-05\n",
      "Epoch 986/1000\n",
      "6/6 [==============================] - 0s 665us/sample - loss: 3.2470e-05\n",
      "Epoch 987/1000\n",
      "6/6 [==============================] - 0s 332us/sample - loss: 3.2233e-05\n",
      "Epoch 988/1000\n",
      "6/6 [==============================] - 0s 1ms/sample - loss: 3.1998e-05\n",
      "Epoch 989/1000\n",
      "6/6 [==============================] - 0s 498us/sample - loss: 3.1765e-05\n",
      "Epoch 990/1000\n",
      "6/6 [==============================] - 0s 665us/sample - loss: 3.1534e-05\n",
      "Epoch 991/1000\n",
      "6/6 [==============================] - 0s 498us/sample - loss: 3.1304e-05\n",
      "Epoch 992/1000\n",
      "6/6 [==============================] - 0s 498us/sample - loss: 3.1075e-05\n",
      "Epoch 993/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 3.0849e-05\n",
      "Epoch 994/1000\n",
      "6/6 [==============================] - 0s 665us/sample - loss: 3.0624e-05\n",
      "Epoch 995/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 3.0401e-05\n",
      "Epoch 996/1000\n",
      "6/6 [==============================] - 0s 1ms/sample - loss: 3.0180e-05\n",
      "Epoch 997/1000\n",
      "6/6 [==============================] - 0s 4ms/sample - loss: 2.9960e-05\n",
      "Epoch 998/1000\n",
      "6/6 [==============================] - 0s 499us/sample - loss: 2.9742e-05\n",
      "Epoch 999/1000\n",
      "6/6 [==============================] - 0s 1ms/sample - loss: 2.9525e-05\n",
      "Epoch 1000/1000\n",
      "6/6 [==============================] - 0s 1ms/sample - loss: 2.9310e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x20104c25b88>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 生成测试数据\n",
    "xs = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], dtype=float)\n",
    "ys = np.array([1.0, 1.5, 2.0, 2.5, 3.0, 3.5], dtype=float)\n",
    "# 训练模型\n",
    "model.fit(xs,ys,epochs=1000) # 指定迭代的次数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如上图可以看到loss在不断地减少，非常好"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用模型进行预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.007809]], dtype=float32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([7.0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如上的结果是预测房价为40万左右符合"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
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